Dynamic connectedness between financial assets in Turkey: Evidence from the TVP-VAR model

Abstract

This study aims to investigate the dynamic connectedness relations between financial assets in Turkey from the perspective of Covid-19. In order to determine this, daily data between 2008 and 2021 of six sub-markets, which can be indicators about the economy, including money, bonds, foreign exchange, stocks, commodities and credit risk, were used. TVP-VAR model results show that the dynamic interconnectedness relationship between related financial assets increases during turbulence periods experienced at both the global and local levels in the sample period. This result can be interpreted as the stress occurring in related financial assets during stress periods increases the total risk as a result of increased linkage with other assets. As a result of the research, the foreign exchange market and credit risk indicators are found to be the net shock transmitter in the sample period; money, bond and commodity market indicators were found to be net shock receivers. On the other hand, it has been observed that the stock market changes frequently as shock receivers and transmitters over time and is neutral in terms of average value over time.

Keywords

• TVP-VAR
• Financial Markets
• Dynamic Connectedness
• Covid-19

Türkiye’de finansal varlıklar arasında dinamik bağlantılılık: TVP-VAR modelinden kanıtlar

Abstract

Bu çalışma ile Türkiye’deki finansal varlıklar arasındaki dinamik bağlantılılık ilişkileri Covid-19 perspektifinde araştırılmak istenmektedir. Bunu tespit edebilmek amacıyla para, tahvil, döviz, hisse senedi, emtia ve kredi riski olmak üzere ekonomi hakkında gösterge olabilecek altı alt piyasanın, 2008 ile 2021 tarihleri arasındaki günlük verileri kullanılmıştır. TVP-VAR model sonuçları, örneklem döneminde hem küresel hem de yerel düzeyde yaşanan türbülans dönemlerinde ilgili finansal varlıklar arasındaki dinamik bağlantılılık ilişkisinin arttığını göstermektedir. Bu sonuç, stres dönemlerinde ilgili finansal varlıklarda meydana gelen stresin diğer varlıklarla artan bağlantı sonucu toplam riski arttırdığı şeklinde yorumlanabilir. Araştırma sonucunda döviz piyasası ile kredi riski göstergeleri, örneklem dönemi içerisinde net şok yayıcısı; para, tahvil ve emtia piyasaları göstergelerinin ise net şok alıcısı olduğu tespit edilmiştir. Pay piyasasının ise zaman içerisinde şok alıcı ve verici olarak sıklıkla değiştiği ve zaman içerisindeki ortalama değer bakımından nötr olduğu gözlemlenmiştir.

Keywords

• TVP-VAR
• Finansal Piyasalar
• Dinamik Bağlantılılık
• Covid-19

References

1. Adekoya, O. B. ve Oliyide, J. A. (2021). “How COVID-19 Drives Connectedness Among Commodity and Financial Markets: Evidence from TVP-VAR and Causality-in-quantiles Techniques”, Resources Policy, (70), 1-17.
2. Akdeniz, C, Çatık, N. (2019). “Parasal Aktarım Mekanizmalarının İşleyişinde Finansal Koşulların Önemi: TVP-VAR Modellerinden Bulgular”, Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (34), 73-96.
3. Bajo-Rubio, O., Berke, B. ve McMillan, D. (2017), “The Behaviour of Asset Return and Volatility Spillovers in Turkey: A Tale of Two Crises”, Research in International Business and Finance, 41(C), 577-589.
4. Ben Amar, A., Bélaïd, F., Ben Youssef, A., ve Guesmi, K. (2020). “Connectedness Among Regional Financial Markets in the Context of the COVID-19”, Applied Economics Letters, 1-8.
5. Çatık, A. N. (2020). “A Time-varying VAR Investigation of the Relationship Among Electricity, Fossil Fuel Prices and Exchange Rate in Turkey”, Journal for Economic Forecasting, Institute for Economic Forecasting, 23 (3), 60-77,
6. Çatık, A.N. ve Karaçuka, M. (2012). “The Bank Lending Channel In Turkey: Has It Changed After The Low-İnflation Regime?”, Applied Economics Letters, 19(13), 1237-1242.
7. Çelik, O., Erer, E. ve Erer, D. (2021), “The Role of COVID-19 Outbreak in the Pass-Through Effect of Monetary Policy to Unemployment in the US: An Analysis with Time Varying Parameters-VAR and Wavelet Coherence Methods”, 7th International Conference on Economics, Turkish Economic Association.
8. Çiçek, M. (2005), “Türkiye’de Parasal Aktarım Mekanizması: Var (Vektör Otoregrasyonu) Yaklaşımıyla Bir Analiz”, İktisat İşletme ve Finans, 20 (233), 82-105.

9. Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158-171.
10. Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of forecasting, 28(1), 57-66.
11. Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of econometrics, 182(1), 119-134.
12. Kang, W., Ratti, R. A. ve Yoon, K. H. (2015), “Time-varying Effect of Oil Market Shocks on The Stock Market”, Journal of Banking & Finance, 61 (2), S150–S163.
13. Karabıyık, C. (2020). “Türkiye’de Borsa, Emtia, Tahvil ve Döviz Piyasaları Arasındaki Etkileşim: Yayılım Endeksi Yaklaşımı”, Yönetim ve Ekonomi Araştırmaları Dergisi, 18 (4), 265-284.
14. Koop, G., Leon-Gonzalez, R. ve Strachan, R. W. (2009), “On The Evolution of the Monetary Policy Transmission Mechanism”, Journal of Economic Dynamics and Control, 33 (4), 997-1017.
15. Li, X., Li, B., Wei, G., Bai, L., Wei, Y. ve Liang, C. (2021). “Return Connectedness Among Commodity and Financial Assets During the COVID-19 Pandemic: Evidence from China and the US”, Resources Policy, 73, 1-16.
16. Naeem, M. A., Mbarki, I., Alharthi, M., Omri, A., & Shahzad, S. J. H. (2021). “Did COVID-19 Impact the Connectedness Between Green Bonds and Other Financial Markets? Evidence From Time-Frequency Domain With Portfolio Implications”, Frontiers in Environmental Science, 9, 1-15.
17. Nakajima, J. (2011), “Time-Varying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications,” IMES Discussion Paper Series 11-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
18. Nasir, M. A., Rizvi, S. A. ve Rossi, M. (2018), “A Treatise on Oil Price Shocks and Their Implications for The UK Financial Sector: Analysis Based on Time-varying Structural VAR Model”, The Manchester School, 86 (5), 586-621.
19. Örnek, İ. (2009), “Türkiye’de Parasal Aktarım Mekanizması Kanallarının İşleyişi”, Maliye Dergisi. 156, 104-125.
20. Periola-Fatunsin, O., Oliyide, J. A., ve Fasanya, I. O. (2021). “Uncertainty Due to Pandemic and the Volatility Connectedness Among Asian REITs Market”, Asian Economics Letters, 2 (2), 1-5.
21. Polat, O. (2020a). “Time-varying Propagations Between Oil Market Shocks and a Stock Market: Evidence from Turkey”, Borsa Istanbul Review, 20(3), 236–243.
22. Polat, O. (2020b). “Petrol Fiyat Şokları ve Finansal Aktivite: TVP-VAR Yaklaşımı”, BMIJ, 8(2): 1922-1943. Primiceri, G. E. (2005), “Time Varying Structural Vector Autoregressions and Monetary Policy”, The Review of Economic Studies, 72 (3), 821–852.
23. So, Mike K.P., Chu, Amanda M.Y. ve Chan, Thomas W.C., 2021. "Impacts of the COVID-19 Pandemic on Financial Market Connectedness," Finance Research Letters, 38 (C), 1-8.
24. Toparlı, E. A., Çatık, A. N. ve Balcılar, M. (2019). “The Impact of Oil Prices on The Stock Returns in Turkey: A TVP-VAR Approach” Physica A: Statistical Mechanics and its Applications, 535(C), 1-12.
25. Tören, E. (2014). “The Impact of Stock Prices on Consumption and Interest Rate in Turkey: Evidence from a Time Varying Vector Autoregressive Model”, in Proceedings of International Conference of Eurasian Economies 2014, 274-281, Skopje, MACEDONIA.
26. Yılmaz, A. ve Altay, H. (2016), “İthal Ham Petrol Fiyatları ve Döviz Kuru Arasındaki Eşbütünleşme ve Oynaklık Yayılma Etkisinin İncelenmesi: Türkiye Örneği”, Ege Akademik Bakış, 16 (4), 655-671.
27. Youssef, M., Mokni, K. ve Ajmi, A. N. (2021). “Dynamic Connectedness Between Stock Markets in the Presence of the COVID‑19 Pandemic: Does Economic Policy Uncertainty Matter?”, Financial Innovation, 7 (1): 13.